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https://www.sciencedirect.com/science/article/pii/S0307904X08001844
On the other hand, electric load forecasting model ought to contain several social factors to increase its explanation capabilities, i.e., multivariate forecasting models, such as social activities and seasonal factors could be introduced into the SVRIA model to forecast electric load.Cited by: 262
https://www.researchgate.net/publication/222768707_Electric_load_forecasting_by_support_vector_model
A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector …Author: Wei-Chiang Hong
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3891232/
Dec 26, 2013 · Electricity load forecasting is an important issue to operate the power system reliably and economically. In this study, to improve forecasting accuracy of electricity load forecasting using support vector regression (SVR), a firefly algorithm (FA) …Cited by: 55
https://www.sciencedirect.com/science/article/abs/pii/S0307904X08001844
Therefore, a rigid forecasting approach with strong general nonlinear mapping capabilities is essential. Support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the generalization errors, rather than minimizing the training errors which are used by ANNs.Cited by: 262
https://www.researchgate.net/publication/224677810_Support_Vector_Machine_Model_in_Electricity_Load_Forecasting
With load forecasting at the top layer implemented by the recursive least square support vector machines (RLS-SVM) algorithm, discrete state-space equations are established to …
https://www.sciencedirect.com/science/article/pii/S0306261917302581
Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings ... Fan SunElectric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing, 173 (2016), pp. 958-970. Google Scholar.Cited by: 91
https://www.sciencedirect.com/science/article/pii/S0307904X08001844
On the other hand, electric load forecasting model ought to contain several social factors to increase its explanation capabilities, i.e., multivariate forecasting models, such as social activities and seasonal factors could be introduced into the SVRIA model to forecast electric load.
https://www.researchgate.net/publication/222768707_Electric_load_forecasting_by_support_vector_model
A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3891232/
Dec 26, 2013 · Electricity load forecasting is an important issue to operate the power system reliably and economically. In this study, to improve forecasting accuracy of electricity load forecasting using support vector regression (SVR), a firefly algorithm (FA) …
https://www.sciencedirect.com/science/article/abs/pii/S0307904X08001844
Therefore, a rigid forecasting approach with strong general nonlinear mapping capabilities is essential. Support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the generalization errors, rather than minimizing the training errors which are used by ANNs.
https://www.researchgate.net/publication/224677810_Support_Vector_Machine_Model_in_Electricity_Load_Forecasting
With load forecasting at the top layer implemented by the recursive least square support vector machines (RLS-SVM) algorithm, discrete state-space equations are established to …
https://www.sciencedirect.com/science/article/pii/S0306261917302581
Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings ... Fan SunElectric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing, 173 (2016), pp. 958-970. Google Scholar.
https://onlinelibrary.wiley.com/doi/abs/10.1002/er.787
2016 International Conference on Electrical and Information Technologies (ICEIT), (2016). Malek Sarhani and Abdellatif El Afia Feature selection and parameter optimization of support vector regression for electric load forecasting 28810.1109/EITech.2016.75196082016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), (2016).
https://www.sciencedirect.com/science/article/pii/S0360544211004634
This investigation presents an electric load forecasting model which combines the seasonal recurrent support vector regression model with chaotic artificial bee colony algorithm (namely SRSVRCABC) to improve the forecasting performance.
https://www.sciencedirect.com/science/article/pii/S019689040800318X
Thus, it is worth analyzing where these forecasts fail and how forecasting accuracy is improved. Based on authors’ series research on applications of support vector regression in electric load forecasting, SVR with evolutionary algorithms is a superior alternative to improve the load forecasting …
https://link.springer.com/article/10.1007/s11071-019-05252-7
Sep 20, 2019 · Abstract. Accurate electric load forecasting can provide critical support to makers of energy policy and managers of power systems. The support vector regression (SVR) model can be hybridized with novel meta-heuristic algorithms not only to identify fluctuations and the nonlinear tendencies of electric loads, but also to generate satisfactory forecasts.
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